# A two-stage system ranks ports by expected tropical-cyclone impacts [Canonical reading](https://escapemeridian.com/readings/a-two-stage-system-ranks-ports-by-expected-tropical-cyclone-c9ab3b67) The paper’s authors present a two-stage recommendation framework for estimating which ports a tropical cyclone may affect and where disruption may last longest. The first stage selects candidate ports near the expected landfall location; the second ranks them by estimated impact days. This matters for preparedness because authorities facing one storm may need to prioritize inspections and recovery resources across several ports, while historical observations at individual ports are limited and landfall locations uncertain. The authors use AIS-derived impact days for 37 ports and 31 tropical cyclones in the Gulf of Mexico study region. In tests on Hurricanes Nicole and Ian, they report that the retrieval stage identified all observed affected ports under landfall uncertainty. Among the ranking models, Wide & Deep Learning came closest to the observed rankings, with mean ranking agreement above a distance-based baseline by 85.5% for Nicole and 55.5% for Ian. The authors also report that its rankings were less sensitive to landfall uncertainty. These results motivate the authors’ suggestion that the rankings could help port authorities prioritize post-cyclone inspections and allocate recovery resources. The abstract does not establish how well the approach would work in operational planning. Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv cs.AI · Article Original source date: Nov 5, 2026 (2026-11-05). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-08T18:08:42.303Z. First observed: 2026-11-06T05:10:07.607Z. Retrieved: 2026-11-08T18:07:51.921Z. - [Robust Ranking of Port Impacts from Impending Tropical Cyclones: A Two-Stage Recommendation System](https://arxiv.org/abs/2611.07296) ## Limits The abstract reports tests on Hurricanes Nicole and Ian; it does not state how the approach performs in operational planning. The results are described for the Gulf of Mexico study region and the ports and tropical cyclones used in the study. ## Open question How well would the reported rankings match observed port impacts in tropical cyclones beyond Hurricanes Nicole and Ian? ## Source-stated The authors propose a two-stage framework that selects candidate ports by proximity to expected landfall and ranks them by estimated impact days. > first selects candidate ports based on their proximity to the expected landfall location and then ranks them by their estimated number of impact days - [Supporting source](https://arxiv.org/abs/2611.07296) ## Source-stated The study uses AIS-derived impact days for 37 ports and 31 tropical cyclones in the Gulf of Mexico study region. > Using AIS-derived impact days for 37 ports and 31 TCs in the Gulf of Mexico study region - [Supporting source](https://arxiv.org/abs/2611.07296) ## Source-stated For Hurricanes Nicole and Ian, the authors report that retrieval identified all observed affected ports under landfall uncertainty. > In tests on Hurricanes Nicole and Ian, the retrieval stage consistently identifies all observed affected ports under landfall uncertainty. - [Supporting source](https://arxiv.org/abs/2611.07296) ## Source-stated Among the ranking models, Wide & Deep Learning had the closest agreement with observed port rankings; its mean ranking agreement exceeded the distance-based baseline by the reported amounts for Nicole and Ian. > Its mean ranking agreement exceeds that of the distance-based baseline by 85.5% for Nicole and 55.5% for Ian - [Supporting source](https://arxiv.org/abs/2611.07296) ## Source-stated The authors suggest the rankings could help port authorities prioritize inspections and allocate recovery resources across ports threatened by the same storm. > These rankings could help port authorities prioritize post-cyclone inspections and allocate recovery resources across ports threatened by the same event. - [Supporting source](https://arxiv.org/abs/2611.07296) ## Source and contributor context Kuai, Chenchen, Tang, Shixuan, Ma, Chaolun, Kong, Xiaoqiang, Wang, Xiubin Bruce, Zhang, Yunlong, Li, Zihao · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Deep learning Machine learning that uses many layers of simple calculations; the basis of most current AI. - [Definition reference](https://en.wikipedia.org/wiki/Deep_learning) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 0e016484-d230-46d0-8571-6685705ac1cd. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-08T18:08:42.303Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 95fb37ca3405e68d328e4ca4edff2a0b7901f4aac363119b448adcc460ad3df6. Source text hash: c6cd6e83d7ade920850d5708abc9ad31c20ea3de1077ad6875159c8cfd97190d. ## Revision and source-check history ### Version 1 — 2026-11-08T18:08:42.303Z The paper’s authors present a two-stage recommendation framework for estimating which ports a tropical cyclone may affect and where disruption may last longest. The first stage selects candidate ports near the expected landfall location; the second ranks them by estimated impact days. This matters for preparedness because authorities facing one storm may need to prioritize inspections and recovery resources across several ports, while historical observations at individual ports are limited and landfall locations uncertain. The authors use AIS-derived impact days for 37 ports and 31 tropical cyclones in the Gulf of Mexico study region. In tests on Hurricanes Nicole and Ian, they report that the retrieval stage identified all observed affected ports under landfall uncertainty. Among the ranking models, Wide & Deep Learning came closest to the observed rankings, with mean ranking agreement above a distance-based baseline by 85.5% for Nicole and 55.5% for Ian. The authors also report that its rankings were less sensitive to landfall uncertainty. These results motivate the authors’ suggestion that the rankings could help port authorities prioritize post-cyclone inspections and allocate recovery resources. The abstract does not establish how well the approach would work in operational planning. ## Original source arXiv cs.AI · Article Original source date: Nov 5, 2026 (2026-11-05). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-08T18:08:42.303Z. First observed: 2026-11-06T05:10:07.607Z. Retrieved: 2026-11-08T18:07:51.921Z. - [Robust Ranking of Port Impacts from Impending Tropical Cyclones: A Two-Stage Recommendation System](https://arxiv.org/abs/2611.07296) ## Limits The abstract reports tests on Hurricanes Nicole and Ian; it does not state how the approach performs in operational planning. The results are described for the Gulf of Mexico study region and the ports and tropical cyclones used in the study. ## Open question How well would the reported rankings match observed port impacts in tropical cyclones beyond Hurricanes Nicole and Ian? ## Source-stated The authors propose a two-stage framework that selects candidate ports by proximity to expected landfall and ranks them by estimated impact days. > first selects candidate ports based on their proximity to the expected landfall location and then ranks them by their estimated number of impact days - [Supporting source](https://arxiv.org/abs/2611.07296) ## Source-stated The study uses AIS-derived impact days for 37 ports and 31 tropical cyclones in the Gulf of Mexico study region. > Using AIS-derived impact days for 37 ports and 31 TCs in the Gulf of Mexico study region - [Supporting source](https://arxiv.org/abs/2611.07296) ## Source-stated For Hurricanes Nicole and Ian, the authors report that retrieval identified all observed affected ports under landfall uncertainty. > In tests on Hurricanes Nicole and Ian, the retrieval stage consistently identifies all observed affected ports under landfall uncertainty. - [Supporting source](https://arxiv.org/abs/2611.07296) ## Source-stated Among the ranking models, Wide & Deep Learning had the closest agreement with observed port rankings; its mean ranking agreement exceeded the distance-based baseline by the reported amounts for Nicole and Ian. > Its mean ranking agreement exceeds that of the distance-based baseline by 85.5% for Nicole and 55.5% for Ian - [Supporting source](https://arxiv.org/abs/2611.07296) ## Source-stated The authors suggest the rankings could help port authorities prioritize inspections and allocate recovery resources across ports threatened by the same storm. > These rankings could help port authorities prioritize post-cyclone inspections and allocate recovery resources across ports threatened by the same event. - [Supporting source](https://arxiv.org/abs/2611.07296) ## Source and contributor context Kuai, Chenchen, Tang, Shixuan, Ma, Chaolun, Kong, Xiaoqiang, Wang, Xiubin Bruce, Zhang, Yunlong, Li, Zihao · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Deep learning Machine learning that uses many layers of simple calculations; the basis of most current AI. - [Definition reference](https://en.wikipedia.org/wiki/Deep_learning) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 0e016484-d230-46d0-8571-6685705ac1cd. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-08T18:08:42.303Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 95fb37ca3405e68d328e4ca4edff2a0b7901f4aac363119b448adcc460ad3df6. Source text hash: c6cd6e83d7ade920850d5708abc9ad31c20ea3de1077ad6875159c8cfd97190d. ### passed — 2026-11-08T18:08:42.303Z — anthropic/claude-opus-5.5 Publication: 0e016484-d230-46d0-8571-6685705ac1cd attribution — pass: The reading consistently credits the paper's authors for the framework, results and suggested use. Results are framed as what the authors report, not as established facts or Escape Meridian's view. > Authors:Chenchen Kuai, Shixuan Tang, Chaolun Ma, Xiaoqiang Kong, Xiubin Bruce Wang, Yunlong Zhang, Zihao Li summary — pass: The title, summary and question match the abstract. The reading explains the two stages in plain language and states the preparedness problem. Its question about storms beyond Nicole and Ian follows from the limited test set. Two terms are kept as written: AIS, the ship-tracking data behind 'impact days', and Wide & Deep Learning. Neither blocks understanding, but both would benefit from term-library tooltips; AIS is a suggested addition. The reading never says outright that 'retrieval stage' means the first, candidate-selection stage, though the meaning can be inferred. > We use a two-stage recommendation framework that first selects candidate ports based on their proximity to the expected landfall location and then ranks them by their estimated number of impact days. date — pass: The originalPublished date of 2026-11-05 matches the arXiv v1 submission date. The feed entry dated 2026-11-06 is correctly not used. > \[Submitted on 5 Nov 2026\] support — pass: Every claim matches the abstract. This covers 37 ports and 31 cyclones, retrieval identifying all affected ports, and Wide & Deep beating the baseline by 85.5% for Nicole and 55.5% for Ian. The use for port authorities stays conditional, as the authors frame it. The reading's caveats about operational use and scope come from the abstract and add no unsupported benefits or costs. Calling the authors' suggestion 'motivated' by the results is a light framing and does not change their position. > Its mean ranking agreement exceeds that of the distance-based baseline by 85.5% for Nicole and 55.5% for Ian and is less sensitive to uncertainty in the landfall location. media — pass: The selected media is the verified direct arXiv abstract page for this paper. It is the most direct way to reach the source. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/c9ab3b6769e2dc4249b5bb6ebe1c7e2fd50d8ec3df32bcb9b243762249b6084b/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/a-two-stage-system-ranks-ports-by-expected-tropical-cyclone-c9ab3b67.json)